In digital holographic microscopy, light wave front information from an illuminated object is digitally recorded as a hologram. By reconstructing an image representation of the object from the hologram, characteristics of the object can be extracted from that image representation. However, such reconstruction relates to a high computational cost that limits the speed of the object characterization.
Extracting particle and distributions characteristics from holograms, bright field images, or Fraunhofer diffraction patterns has already been studied in the past and is generally solved by applying different numerical algorithms involving inversion, nonlinear pattern matching, or performing image analysis decomposition. Since integrals of special functions or an extensive use of Fast Fourier Transformation intervene in most of these algorithms, they all suffer from a tremendous increase in computational cost when they have to be performed at high speed. This severely limits real-time particle characterization in high-throughput applications.